ObjectiveThis study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms.MethodsA retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC).ResultsThe malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost.ConclusionsCombining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.

中文翻译：

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将机器学习病理诊断算法纳入甲状腺超声影像数据，提高恶性甲状腺结节的诊断风险

目的本研究旨在建立一种利用机器学习算法预测恶性甲状腺结节的新模型。方法回顾性研究2018年10月至2020年在咸阳市中心医院接受细针穿刺（FNA）细胞学检查或手术治疗的274例甲状腺结节患者。 . 应用最小绝对收缩和选择算子（套索）回归分析和逻辑分析来筛选和识别变量。采用并比较了六种机器学习算法，包括决策树 (DT)、极端梯度提升 (XGBoost)、梯度提升机 (GBM)、朴素贝叶斯分类器 (NBC)、随机森林 (RF) 和逻辑回归 (LR)在构建预测模型时，结合术前临床特征和超声特征。使用 10 折交叉验证进行内部验证。模型的性能通过接受者操作特征曲线 (AUC) 下的面积、准确度、精确度、召回率、F1 分数、Shapley 附加解释 (SHAP) 图、特征重要性和特征相关性来衡量。通过概率密度函数（PDF）和临床效用曲线（CUC）确定风险分层的最佳临界值。结果研究队列中甲状腺结节的恶性率为53.2%。预测模型由年龄、边缘、形状、回声灶、回声性和淋巴结构成。XGBoost 模型明显优于任何一种机器学习模型，AUC 值为 0.829。根据 PDF 和 CUC，我们建议将 51% 的概率作为确定恶性结节风险分层的阈值，大约 85.6% 的恶性结节患者可以被检测到。同时，约89.8%不必要的活检程序将被省去。最后，基于性能最好的XGBoost ML-ed模型，构建了一个在线网络风险计算器来估计恶性甲状腺结节的个人可能性。结论结合临床特征和超声图像特征，ML算法可以实现恶性甲状腺结节的可靠预测结节。基于XGBoost模型的在线网络风险计算器可以轻松实时识别甲状腺恶性结节的概率，可以辅助临床医生为患者制定个体化管理策略。